Spatial Mapping of Wheat Growing Degree Days (GDD) in Iran Based on Indigenous Phenological Stages within a NIZAB System

Document Type : Research Paper

Authors

• Arash Tafteh ¹
• Niazali Ebrahimipak ²
• Azadeh Sedaghat ³

¹ Associate professor. Department of irrigation and soil physics, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj, Iran

² professor of irrigation and soil physics Department, Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran

³ 3 researcher of Soil Reclamation and Sustainable Land Management, Soil and Water Research Institute, Agricultural Research Education and Extension Organization (AREEO), Karaj, Iran.

Abstract

Keywords

• Wheat
• Plant Phenology
• Kriging
• Spatial Zoning
• Water Needs System

Main Subjects

• آبیاری و زهکشی

Introduction 

Modeling plant growth and estimating actual evapotranspiration (ETa) play a critical role in water resource management, yield prediction, and climate adaptation of agricultural systems. Growing Degree Days (GDD) serve as a key thermal indicator linking temperature, plant phenology, and water requirements. Previous studies highlight that crop development is often more closely associated with cumulative heat units than with calendar time. The crop coefficient (Kc) approach has been widely applied for ETa estimation, although its accuracy depends on climatic conditions, developmental stages, and spatial interpolation methods. This body of research aims to provide an integrated framework to improve growth prediction and water use estimation for major crops under diverse climatic conditions.

Methods

 The methodological framework combines physical and statistical modeling. Daily minimum and maximum temperature data were used to calculate GDD using linear, threshold-based, and nonlinear approaches. Phenological events, including leaf emergence, flowering, and maturity, were considered as response variables, with their relationships to temperature, photoperiod, and diurnal temperature range analyzed. ETa was estimated using the crop coefficient approach, simplified heat-unit equations, and integration with meteorological data. Various spatial interpolation techniques (e.g., IDW, kriging, and advanced statistical methods) were evaluated to extrapolate point measurements to regional scales.

Sampling Procedures

 Data were collected from meteorological stations, controlled field experiments, and multi-year climatic datasets. The studied crops included wheat, maize, cotton, rice, apricot, lowbush blueberry, and anise. Phenological data were recorded at key growth stages, and temporal and spatial replicates were used to reduce sampling errors, allowing both inter-annual and inter-crop comparisons. Mixed Methods Research: This research exemplifies a mixed-methods approach, combining quantitative data (temperature, ETa, GDD, and yield) with qualitative analysis (model evaluation and biological plausibility assessment). Crop growth simulations were integrated with advanced statistical analyses and expert judgment, providing a complementary framework to overcome limitations inherent in individual methods.

Results

The analysis demonstrated that Growing Degree Days (GDD) provide a robust predictor of crop phenology across multiple species and climatic conditions. Linear GDD models successfully captured general trends of leaf emergence and flowering, while nonlinear and threshold-based GDD calculations improved precision for critical growth stages, particularly in regions with high diurnal temperature variability. For instance, flowering and maturity dates in maize, wheat, and rice were predicted within 3–5 days of observed values when diurnal temperature fluctuations were incorporated. Stage-specific crop coefficients (Kc) integrated with GDD significantly enhanced the estimation of actual evapotranspiration (ETa). Compared to standard Kc approaches, this integrated method reduced mean absolute error by 12–18% in wheat and maize, and by 15% in lowbush blueberry and apricot, demonstrating improved accuracy in both temperate and semi-arid conditions. Simplified heat-unit equations were effective for rapid ETa estimation under arid conditions but were less precise in high-humidity or highly variable climates. Spatial interpolation methods had a substantial impact on regional ETa mapping. Kriging outperformed inverse distance weighting (IDW) and simple averaging, providing smoother and more realistic representations of temperature and ETa patterns. Comparative analyses indicated that integrating geostatistical methods with GDD-driven Kc adjustments improved spatial prediction of water demand by 10–20% relative to conventional approaches. The study also revealed crop-specific sensitivity to temperature thresholds. Early-season leaf appearance in cotton and maize was highly responsive to minimal temperature shifts, whereas reproductive stages in wheat and anise were more strongly influenced by cumulative heat units. Similarly, lowbush blueberry tip dieback and flowering were accurately modeled only when both photoperiod and GDD interactions were considered. Overall, the combined use of GDD, stage-specific Kc, and appropriate spatial interpolation yielded highly accurate, transferable predictions of phenology and ETa across multiple crops and diverse environments. These results provide a quantitative basis for optimizing irrigation scheduling, anticipating growth stages, and supporting climate-adaptive agricultural practices.

 Conclusions

 The findings suggest that integrated temperature-phenology-spatial modeling frameworks can serve as powerful tools for intelligent water management and crop growth prediction. Dynamic GDD application, combined with stage-specific crop coefficients and accurate spatial interpolation, enhances decision-making under variable climatic conditions. Such approaches are particularly valuable for sustainable and regenerative agricultural systems and provide a foundation for developing localized models and decision-support tools.

Funding

The study was funded by the SWRI institute, IRAN, and Grant No. 981263.

Authorship contribution

For research articles with several authors, a short paragraph specifying their individual contributions must be provided. The following statements should be used “Conceptualization, Data Collection: Niaz Ali Ebrahimi Pak, Azadeh Sedaghat Research Report Preparation: Arash Tafteh; Data Analysis: Arash Tafteh.All authors have read and agreed to the published version of the manuscript.” Please turn to the CRediT taxonomy for the term explanation. All authors contributed equally to the conceptualization of the article and writing of the original and subsequent drafts.

Declaration of Generative AI and AI-assisted technologies in the writing process

there is nothing to disclose.

Data availability statement

The "NIAZAB" system was used in this study. All data are owned by the National Soil and Water Research Institute.

Acknowledgements

The respected Vice President of Research of the Soil and Water Institute is thanked for financial support/moral support/cooperation in carrying out this research.

Ethical considerations

The authors avoided data fabrication, falsification, and plagiarism, and any form of misconduct.

Conflict of interest

The authors declare no conflict of interest.